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Fundamentals of Machine Learning through Python

  • Python, Scikit-Learn, and Practical ML: From Basics to Projects
  • New
  • Rating: 4.8 out of 54.8 (40 ratings)
  • 1,122 students
  • 1hr 36min of on-demand video
  • Created by Meenakshi Nair
  • English

What you’ll learn

  • Learn the art of data cleaning, handling missing values, and feature engineering to ensure high-quality datasets for effective machine learning model training
  • Develop a solid understanding of Python essentials, control structures, and modular programming, providing a strong foundation for machine learning applications
  • Dive into supervised learning techniques, mastering linear regression for numerical predictions, and logistic regression for effective classification
  • Gain proficiency in assessing and optimizing model performance through cross-validation, addressing overfitting and underfitting, and fine-tuning
  • Delve into ensemble methods such as Random Forest, Gradient Boosting, Support Vector Machine
  • Apply acquired skills to a practical project, guiding learners through data preprocessing, model selection, training, and evaluation


  • Basic knowledge of Python programming is recommended, but this beginner-friendly course welcomes learners with no prior machine learning experience


Unlock the potential of machine learning with our comprehensive course, “Mastering Machine Learning: From Fundamentals to Practical Projects with Python and Scikit-Learn.” Tailored for aspiring data enthusiasts and programmers, this course is an immersive journey through the key pillars of machine learning, ensuring a strong foundation and practical proficiency.

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Begin with Python fundamentals, covering variables, control structures, and modular programming, before delving into the heart of data science: data preparation. Learn to wield Python for data cleaning, handle missing values, and engineer features to optimize dataset quality. Transition seamlessly into supervised learning, mastering linear and logistic regression for numerical predictions and categorical classifications.

Navigate the intricate landscape of model evaluation and validation, ensuring your models generalize well to unseen data. Harness the power of Scikit-Learn, building and training models with its intuitive interface. Explore advanced topics, from ensemble methods like Random Forest and Gradient Boosting to the complexity-solving capabilities of Support Vector Machines.

The course crescendos with a hands-on project, where learners apply acquired skills to real-world scenarios, from data preprocessing to model selection and evaluation. Emerging from this course, you’ll possess the confidence to navigate the machine learning landscape, equipped with practical skills, project experience, and a deepened understanding of Python and Scikit-Learn. Start your machine learning journey today!

Who this course is for:

  • This course is designed for aspiring data enthusiasts, programmers, and beginners in machine learning who seek a comprehensive introduction to the field. Whether you’re a Python novice or looking to transition into data science, this beginner-friendly journey will equip you with the essential skills to confidently explore and apply machine learning concepts in real-world scenarios.

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Course content

9 sections • 26 lectures • 1h 46m total lengthCollapse all sections

Introduction3 lectures • 7min

  • Introduction to Course02:05
  • Setting Up Google Colaboratory02:48
  • Importance of Machine Learning02:31

Python Fundamentals for Machine Learning6 lectures • 22min

  • Introduction to Python01:19
  • Variables and Operators02:34
  • Control Structures06:33
  • Functions05:18
  • Modules01:46
  • Intro to Data Structures04:00
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Data Preparation: The Foundation of ML Success3 lectures • 24min

  • Introduction to Data Processing01:10
  • Transforming Data15:53
  • Data Visualization07:12

Supervised Learning3 lectures • 17min

  • Introduction to supervised learning01:48
  • Linear Regression09:45
  • Logistic Regression04:57

Model Evaluation and Optimization4 lectures • 13min

  • Metrics06:29
  • Cross Validation03:42
  • Overfitting or Underfitting Models01:43
  • Hyperparameter Tuning01:30

Scikit-Learn2 lectures • 3min

  • Introduction to scikit-learn00:47
  • Overview of documentation02:02

Advanced Machine Learning Models3 lectures • 17min

  • RandomForest and GradientBoosting11:02
  • KNN03:42
  • SVM02:37

Project1 lecture • 2min

  • Project Introduction01:58
  • Project Submission3 questions

Conclusion1 lecture • 1min

  • Concluding Remarks01:22

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